Classification of target objects in motion

ABSTRACT

A method for classifying objects in motion that includes providing, to a processor, feature data for one or more classes of objects to be classified, wherein the feature data is indexed by object class, orientation, and sensor. The method also includes providing, to the processor, one or more representative models for characterizing one or more orientation motion profiles for the one or more classes of objects in motion. The method also include acquiring, via a processor, feature data for a target object in motion from multiple sensors and/or for multiple times and trajectory of the target object in motion to classify the target object based on the feature data, the one or more orientation motion profiles and the trajectory of the target object in motion.

FIELD OF THE INVENTION

The currently described invention relates to systems and methods forclassifying objects in motion using more than a single sensorobservation either by using multiple sensors, or multiple observationsfrom a single sensor, or both. The currently described invention alsorelates to data fusion.

BACKGROUND

Prior art methods for classifying target objects in motion (e.g.,flight) involve generating training data for the classification systemsin an offline mode. The training data, acquired for exemplary objectsused for classifying target objects is generated prior to observing thetarget object to be classified. When fusing data from multiple sensorsand multiple observations, the training data consists of joint featuredistributions; joint over features to be collected from each sensor andeach observation time. One approach attempts to capture the statisticaldependencies between multiple observations of exemplary objects withpredictable flight dynamics by training over a subset of all possibletrajectories and sensor observation times for a fixed set of sensorlocations. This method produces a sparse data set since the number ofpossible trajectories and observation times is infinitely large and thedimensionality of the joint feature space is likewise large and unknownsince numbers and times of observation are unknown prior to observationof target object in flight. Therefore, the method does not adequatelyrepresent the statistics to successfully classify the target objects. Analternative approach does not attempt to capture the dependenciesbetween multiple observations, but assumes sub-optimally that theobservations are independent.

These methods do not adequately classify the target objects. A needtherefore exists for improved systems and methods for classifyingobjects in motion using multiple observations.

SUMMARY

Embodiments described herein relate generally to systems and methods forclassifying objects in motion.

One embodiment features a method for classifying objects in motion usingmultiple observations (e.g., from multiple sensors and/or multipletimes). The method includes providing, to a processor, feature data forone or more classes of objects to be classified, wherein the featuredata is indexed by object class, orientation, and sensor. The methodalso includes providing, to the processor, one or more representativemodels for characterizing one or more orientation motion profiles forthe one or more classes of objects in motion. The method also includeacquiring, via a processor, feature data for a target object in motionand trajectory of the target object in motion to classify the targetobject based on the feature data, the one or more orientation motionprofiles and the trajectory of the target object in motion.

In some embodiments, providing the one or more representative models forcharacterizing one or more orientation motion profiles includesacquiring orientation motion data for an exemplary object in motion. Insome embodiments, providing the one or more representative models forcharacterizing one or more orientation motion profiles includesgenerating orientation motion data based on an analytical model for anexemplary object in motion.

In some embodiments, the method includes acquiring the feature data forthe target object in motion and the trajectory of the target object inmotion at one or more instances of time, periods of time, or acombination of both. In some embodiments, the method includes acquiringthe feature data for the target object in motion and the trajectory ofthe target object in motion using a plurality of sensors.

In some embodiments, the feature data includes at least one of radardata, optical data or infrared data for each of the one or more classesof objects. The feature data can include radar cross section signals andtime derivatives of the radar cross section signals. The sensor can be aradar system, lidar system, optical imaging system, or infraredmonitoring system.

In some embodiments, the method includes classifying the target objectusing Bayes' Rule the target object as belonging to a particular classof the one or more classes of objects based on the posterior probabilitythe target object corresponds to the particular class. In someembodiments, the feature data for the one or more classes of objects andthe one or more representative models for characterizing one or moreorientation motion profiles for the feature data are indexed in adatabase stored on the processor.

Another embodiment features a system for classifying objects in motion.The system including data collected prior to classifying a target objectin motion, wherein the data includes a) feature data on one or moreclasses of objects to be classified, wherein the feature data is indexedby orientation, sensor, and object class; and b) one or morerepresentative models for characterizing one or more orientation motionprofiles for the feature data on the one or more classes of objects. Thesystem also includes at least one sensor to acquire feature data for atarget object in motion and trajectory of the target object in motion.The system also includes a first processor to generate reference featuredata while the target object is in motion based on the object class andthe trajectory of the target object in motion, wherein the at least onesensor provides feature data and time of feature data. The system alsoincludes a second processor to classify the target object during motionof the target object based on the reference feature data generated bythe first processor, and feature data for the target object in motion.

In some embodiments, the system includes one or more sensors to acquireorientation motion data for an exemplary object in motion to generatethe one or more orientation motion profiles for the one or more classesof objects.

In some embodiments, the first processor generates reference featurevectors by: 1. selecting an object class, 2. selecting a orientationmotion profile for the selected object class, 3. for each point in timethat feature data is collected by the at least one sensor for the targetobject in motion, the selected orientation motion profile and thetrajectory of the target object in motion are used to determine theorientation of the target object in motion, a feature is selected fromthe feature database based on the sensor, object class, and orientationof the target object in motion. In some embodiments, steps 1-3 arerepeated to generate a collection of reference feature vectors.

In some embodiments, the second processor is configured to performBayesian classification using the reference feature data generated bythe first processor as a priori data and the feature data for the targetobject to be classified to generate posterior object class typeprobabilities. In some embodiments, the feature data for the targetobject in motion includes feature data collected from each sensor atsingle points in time.

Other aspects and advantages of the current invention will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating the principles of theinvention by way of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of various embodiments of the invention will bemore readily understood by reference to the following detaileddescriptions in the accompanying drawings, in which:

FIG. 1 is a schematic illustration of a system for classifying objects,according to an illustrative embodiment.

FIG. 2A is a flowchart of a prior art method for classifying objects inmotion.

FIG. 2B is a flowchart of a method for classifying objects in motion,according to an illustrative embodiment.

FIG. 3A is a graphical representation of radar cross section measured bya first sensor versus radar cross section measured by a second sensor.

FIG. 3B is a graphical representation of radar cross section versusaspect angle for two classes of objects, using a method in oneembodiment for a target object to be classified.

FIG. 3C is a graphical representation of rotation rate versusprobability density for the two classes of objects of FIG. 3B, using amethod in one embodiment for a target object to be classified.

FIG. 3D is a graphical representation of simulated measurements for atarget object in motion.

FIG. 3E is a graphical representation identifying the classification ofa target object based on the feature data and motion profile data ofFIGS. 3B and 3C and measurements for a target object in motion, using amethod to classify the target object.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

FIG. 1 is a schematic illustration of a system 200 for classifyingobjects, according to an illustrative embodiment. The system 200includes two sensors 204 a and 204 b (generally 204) to acquire featuredata and motion profile data for an object 206 (e.g., target object) inmotion. Each sensor 204 is coupled to a corresponding sensor subsystem220. Sensor 204 a is coupled to sensor subsystem 220 a (generally 220)and sensor 204 b is coupled to sensor subsystem 220 b. Each sensor (204a and 204 b) is coupled to a sensor control module (208 a and 208 b,respectively), processor (216 a and 216 b, respectively), input device(240 a and 240 b, respectively), output device (244 a and 244 b,respectively), display device (248 a and 248 b, respectively) and astorage device (252 a and 252 b, respectively).

Processors 216 a and 216 b provide emission signals to sensor controlmodules 208 a and 208 b, respectively, for emission (e.g., radarelectromagnetic emissions) by the sensors 204 a and 204 b. The emittedsignals are directed toward the object 206 in motion. Response signals(e.g., radar response signals) reflected back towards the sensors inresponse to the emitted signals impinging upon the object 206 arereceived by the sensors 204 a and 204 b. The sensor control modules 208a and 208 b receive the response signals from the sensors 204 a and 204b, respectively, and direct the signals to the processor 216 a and 216b, respectively. The processors 216 a and 216 b process the responsesignals received from each of the sensors to, for example, determine thevelocity and radar cross section (RCS) of the object 206. The processors216 a and 216 b store various information regarding the system 200 andits operation in the storage devices 252 a and 252 b, respectively.

The storage devices 252 a and 252 b can store information and/or anyother data associated with the system 200. The storage devices caninclude a plurality of storage devices. The storage devices can include,for example, long-term storage (e.g., a hard drive, a tape storagedevice, flash memory, etc.), short-term storage (e.g., a random accessmemory, a graphics memory, etc.), and/or any other type of computerreadable storage.

The modules and devices described herein can, for example, utilize theprocessors 216 a and 216 b to execute computer executable instructionsand/or include a processor to execute computer executable instructions(e.g., an encryption processing unit, a field programmable gate arrayprocessing unit, etc.). It should be understood that the system 200 caninclude, for example, other modules, devices, and/or processors known inthe art and/or varieties of the illustrated modules, devices, and/orprocessors.

The input devices 240 a and 240 b receive information associated withthe system 200 (e.g., instructions from a user, instructions fromanother computing device) from a user (not shown) and/or anothercomputing system (not shown). The input devices 240 a and 240 b caninclude, for example, a keyboard or a scanner. The output devices 244 aand 244 b output information associated with the system 200 (e.g.,information to a printer (not shown), information to an audio speaker(not shown)).

The display devices 248 a and 248 b display information associated withthe system 200 (e.g., status information, configuration information).The processors 216 a and 216 b execute the operating system and/or anyother computer executable instructions for the system 200 (e.g.,processor 216 a sends transmission signals to the sensor control module208 a for transmission by the sensor 204 a and/or receives responsesignals from the sensor 204 a).

The system 200 also includes a separate processing system 224 that iscoupled to sensor subsystem 220 a and sensor subsystem 220 b. Thestorage device 276 of the processing system 224 contains feature dataand motion profile data on one or more classes of objects (e.g., thefeature data and motion profile data for one or more classes of objectspreviously stored during offline processing steps). In some embodiments,the feature data and motion profile data are previously acquired andstored for one or more exemplary objects. The processing system 224includes a processor 260 that classifies the target object 206 based, inpart, on the previously stored feature data, motion profile data, anddata acquired by the sensors 204 a and 204 b while the target object 206is in motion.

Alternative types of data may be acquired and used by the system 200 inalternative embodiments. In some embodiments, at least one of radar data(acquired using a radar system as the sensor), optical data (acquiredusing an optical imaging system as the sensor) and/or infrared data(acquired using an infrared monitoring system as the sensor) is used forone or more classes of objects.

FIG. 2A is s flowchart 300A of a prior art method for classifying targetobjects in motion. The method includes providing training data (step302), namely an a priori joint feature distribution, to a processor.Joint feature distributions specify the likelihood of obtaining, viasensing, a set of given object features for an object class. Thetraining data, acquired for exemplary objects used for classifyingtarget objects, is generated prior to observing the target object to beclassified. When fusing data from multiple sensors and multipleobservations, the training data consists of joint feature distributions;joint over the features to be collected from each sensor and eachobservation time. In one instance, the classification system attempts tocapture the statistical dependencies (the joint feature distribution)between multiple observations of exemplary objects with predictableflight dynamics by collecting feature data via digital simulation over asubset of all possible trajectories and sensor observation times for afixed set of sensor locations. This method produces a sparse data setsince the number of possible trajectories and observation times isinfinitely large and the dimensionality of the joint feature space islikewise large and unknown since numbers and times of observation isunknown prior to observation of a target object in flight. This methoddoes not adequately represent the statistics (joint featuredistribution) to classify the target objects. An alternative prior artapproach does not attempt to capture the dependencies between multipleobservations, but assumes sub-optimally that the observations areindependent.

The prior art also includes acquiring (step 312), via sensors andprocessors, feature data for a target object in motion and trajectory ofthe target object in motion. The feature data and trajectory for thetarget object in motion can be acquired at, for example, one or moreinstances of time, one or more sensors, or a combination of both.

The prior art also includes classifying (step 324) the target objectbased on the feature data. In one instance, classifying (step 324) thetarget object is performed using a Bayes' classifier in which the targetobject is specified as belonging to a particular class of one or moreclasses of objects based on the posterior probability that the targetobject belongs to the particular class given the a priori joint featuredistribution (step 302) and the target object feature data (step 312).Steps 312 and 324 can be performed, for example, using system 200 ofFIG. 1.

FIG. 2B is s flowchart 300B of a method for classifying target objectsin motion, according to an illustrative embodiment. The method includesproviding (step 304), to a processor, feature data for one or moreexemplary classes of objects to be classified. In one embodiment, thefeature data is indexed by object class, orientation and sensor. Thefeature data can be indexed by, for example, object class, objectorientation, sensor, and/or sensor signal-to-noise ratio. Object classescan include, for example, missile, satellite, ground vehicle, aircraftand/or specific model or type of each of these (e.g., Boeing 747aircraft, F-16 aircraft). The feature data consists of sensor signaldata and can include, for example, radar cross section (RCS) signals,and time derivatives of the RCS signals. The data is generated either byusing one or more digital simulations of sensors and target models or bycollecting the data using one sensor systems and targets. The targetobjects are not required to be in flight to collect features indexed byaspect angle.

The method also includes providing (step 308), to a processor, one ormore representative models for characterizing one or more orientationmotion profiles (motion profile data) for the one or more classes ofobjects in motion. In some embodiments, providing (step 308) the one ormore representative models for characterizing one or more orientationmotion profiles includes the step of acquiring (step 316) orientationmotion data for an exemplary object in motion by digital simulation(e.g., the models are statistical models). In an alternative embodiment,providing (step 308) the one or more representative models forcharacterizing one or more orientation motion profiles includes theoptional step of generating (step 320) orientation motion data based onan analytical model for an exemplary object in motion (e.g., the modelsare physical models). In some embodiments, a combination of statisticalmodels and physical models are used.

In some embodiments, the representative models characterizing a motionprofile for a class of objects is generated based on an analytical modelof an exemplary object in motion. In one embodiment, an analytical modelof an exemplary object in motion is a kinematic model describing themotion of the exemplary object along a curvilinear path in which eachpoint along the path defines the location and velocity vector of theexemplary object. For example, a motion model might consist of aballistic model for propagation of position and velocity and astatistical distribution for the angular momentum vector relative to thevelocity vector, and a statistical distribution for the precessionphase. Examples of ballistic motion models include 4^(th) orderRunge-Kutta integration of differential equations that representgravity, Coriolis and centrifugal forces and the Simplified GeneralPerturbation 4 (SGP4) algorithm.

The feature and motion profile data (steps 304 and 308) are stored in,for example, a database for subsequent use by a real time trainingmethod (e.g., stored by processor 260 in storage device 276 of FIG. 1).

The method also includes generating a representative feature databasespecific to the observed trajectory of the target object to beclassified and the sensor observation times of the target object to beclassified (step 310). The representative feature database is generatedby sampling from the motion profiles of step 308, using them todetermine the orientation of the object to be classified at each sensorcollection and selecting the feature with that orientation, class andSNR from feature data from step 304. In one embodiment, a firstprocessor generates the reference data (e.g., reference feature vectors)by: 1. selecting an object class, 2. selecting a orientation motionprofile for the selected object class, 3. for each point in time thatfeature data is collected by the at least one sensor for the targetobject in motion, the selected orientation motion profile and thetrajectory of the target object in motion are used to determine theorientation of the target object in motion, a feature is selected fromthe feature database based on the sensor, object class, and orientationof the target object in motion. In this way a classification database(namely the joint feature distribution which captures statisticaldependencies) is constructed in near-real time specific to thetrajectory of the object to be classified and the times and locations ofthe sensor observations.

Similar as with the prior art method, the method of FIG. 2B alsoincludes acquiring (step 312), via a processor, feature data for atarget object in motion and trajectory of the target object in motion.The feature data and trajectory for the target object in motion can beacquired at, for example, one or more instances of time, periods oftime, or a combination of both. The feature data and trajectory for thetarget object in motion can be acquired using one or a plurality ofsensors.

Referring to FIG. 2B, the method depicted by flowchart 300B includesclassifying (step 380) the target object based on the joint featuredistribution (generated in step 310) and the a posterior feature data(acquired in step 312). The joint feature distribution (of step 310) isbased on the feature data, the one or more orientation motion profilesand the feature data and trajectory of the target object in motion. Inone embodiment, classifying (step 380) the target object is performedusing Bayes' Rule in which the target object is specified as belongingto a particular class of one or more classes of objects based on theposterior probability the target object belongs to the particular class.Steps 310, 312 and 324 can be performed, for example, using system 200of FIG. 1.

By way of illustration, a simulation was conducted to classify a targetobject using the system 200 of FIG. 1 and the method of FIG. 2B in whichthere are two object classes (triangle and rectangle). Referring to FIG.3D, there are two sensors, sensor 420 a and sensor 420 b (e.g., sensors204 a and 204 b of FIG. 1) for measuring data associated with theobjects in motion.

FIG. 3A illustrates the results using the prior art method of FIG. 2Aand is a graphical representation of the joint feature distribution ofradar cross section (RCS) of the two object classes (triangle andrectangle) measured by a first sensor (sensor 1 is sensor 420 a of FIG.3D) versus radar cross section (RCS) measured by a second sensor (sensor2 is sensor 420 b of FIG. 3D). The method of FIG. 2A assumessub-optimally that the observations are independent (step 302). Thesystem implementing the method of FIG. 2A measures a value of 9 dBsm(decibel measure of the radar cross section of a target relative to onesquare meter) using the first sensor and a value of 15 dBsm using thesecond sensor for a target object (step 312), and is unable toeffectively classify the target object (step 324). The system is unableto determine whether the target object is a rectangle or a trianglebecause the possible feature values (RCS values) for the two objectclasses (rectangle and triangle) overlap (as shown in the plot in FIG.3A).

Feature data and motion profile data was generated for the two classesof objects. FIG. 3B is a graphical representation of radar cross sectionversus aspect angle for the two classes of objects. The data in FIG. 3Bis used as the feature data for the simulation (step 304 of FIG. 2B).FIG. 3C is a graphical representation of rotation rate versusprobability density for the two classes of objects of FIG. 3B. The datain FIG. 3C is used as the motion profile data for the two classes ofobjects (e.g., step 308 of FIG. 2B). In this embodiment, the triangleclass of objects is capable of spinning at a faster rate than therectangular class of objects.

FIG. 3D is a graphical representation of simulated measurements for atarget object 404 in motion. The target object 404 has an initialposition on the Y-axis of the plot 408 at (0,53). The trajectory 412 ofthe target object 404 is at an angle 416 of seventeen (17) degrees. Thespeed of the target object 404 along the trajectory 412 is four (4)km/s. Sensor 420 a observes (i.e., takes a measurement) at a time of two(2) seconds and sensor 420 b observes at a time of five (5) seconds.Sensor 420 a has a signal-to-noise ratio of eight (9) dB and sensor 420b has a signal-to-noise ratio of twelve (15) dB.

The feature data and motion profile data of FIGS. 3B and 3C were thenused as the basis for classifying the target object 404. FIG. 3Eillustrates the results of step 310 of FIG. 2B. FIG. 3E illustrates anew feature database (different from the prior art illustrated by theresult in FIG. 3A) which is constructed after the target object inflight to be classified is observed. The method of the embodimentrepeatedly randomly selects an initial assumption for the target object(triangle or rectangle), initial assumption for the trajectory angle(−90 to 90 degrees) for the target object, and initial assumption of theobject motion profile of the target object rotating at a rate inaccordance with the exemplary rotation rates (degrees/second) of FIG.3C. In this manner, the lightly shaded and darkly shaded points (jointfeature distribution) in FIG. 3E are generated based on orientation ofthe object at the time of sensor measurements and FIG. 3B.

Now, a system employing the inventive methods described herein thatmeasures a value of 9 dBsm (decibel measure of the radar cross sectionof a target relative to one square meter) using the first sensor and avalue of 15 dBsm using the second sensor for a target object is able toeffectively classify the target object as a rectangle (step 380 FIG.3B).

The above-described systems and methods can be implemented in digitalelectronic circuitry, in computer hardware, firmware, and/or software.The implementation can be as a computer program product (i.e., acomputer program tangibly embodied in an information carrier). Theimplementation can, for example, be in a machine-readable storage deviceand/or in a propagated signal, for execution by, or to control theoperation of, data processing apparatus. The implementation can, forexample, be a programmable processor, a computer, and/or multiplecomputers.

A computer program can be written in any form of programming language,including compiled and/or interpreted languages, and the computerprogram can be deployed in any form, including as a stand-alone programor as a subroutine, element, and/or other unit suitable for use in acomputing environment. A computer program can be deployed to be executedon one computer or on multiple computers at one site.

Method steps can be performed by one or more programmable processorsexecuting a computer program to perform functions of the invention byoperating on input data and generating output. Method steps can also beperformed by and an apparatus can be implemented as special purposelogic circuitry. The circuitry can, for example, be a FPGA (fieldprogrammable gate array) and/or an ASIC (application-specific integratedcircuit). Modules, subroutines, and software agents can refer toportions of the computer program, the processor, the special circuitry,software, and/or hardware that implement that functionality.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read-only memory or arandom access memory or both. The essential elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer can include, can beoperatively coupled to receive data from and/or transfer data to one ormore mass storage devices for storing data (e.g., magnetic,magneto-optical disks, or optical disks).

Data transmission and instructions can also occur over a communicationsnetwork. Information carriers suitable for embodying computer programinstructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices. Theinformation carriers can, for example, be EPROM, EEPROM, flash memorydevices, magnetic disks, internal hard disks, removable disks,magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The processor andthe memory can be supplemented by, and/or incorporated in specialpurpose logic circuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer having a display device. The displaydevice can, for example, be a cathode ray tube (CRT) and/or a liquidcrystal display (LCD) monitor. The interaction with a user can, forexample, be a display of information to the user and a keyboard and apointing device (e.g., a mouse or a trackball) by which the user canprovide input to the computer (e.g., interact with a user interfaceelement). Other kinds of devices can be used to provide for interactionwith a user. Other devices can, for example, be feedback provided to theuser in any form of sensory feedback (e.g., visual feedback, auditoryfeedback, or tactile feedback). Input from the user can, for example, bereceived in any form, including acoustic, speech, and/or tactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributing computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The components ofthe system can be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a local area network (LAN), a wide area network (WAN),the Internet, wired networks, and/or wireless networks.

The system can include clients and servers. A client and a server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

Packet-based networks can include, for example, the Internet, a carrierinternet protocol (IP) network (e.g., local area network (LAN), widearea network (WAN), campus area network (CAN), metropolitan area network(MAN), home area network (HAN)), a private IP network, an IP privatebranch exchange (IPBX), a wireless network (e.g., radio access network(RAN), 802.11 network, 802.16 network, general packet radio service(GPRS) network, HiperLAN), and/or other packet-based networks.Circuit-based networks can include, for example, the public switchedtelephone network (PSTN), a private branch exchange (PBX), a wirelessnetwork (e.g., RAN, bluetooth, code-division multiple access (CDMA)network, time division multiple access (TDMA) network, global system formobile communications (GSM) network), and/or other circuit-basednetworks.

The computing device can include, for example, a computer, a computerwith a browser device, a telephone, an IP phone, a mobile device (e.g.,cellular phone, personal digital assistant (PDA) device, laptopcomputer, electronic mail device), and/or other communication devices.The browser device includes, for example, a computer (e.g., desktopcomputer, laptop computer) with a world wide web browser (e.g.,Microsoft® Internet Explorer® available from Microsoft Corporation,Mozilla® Firefox available from Mozilla Corporation). The mobilecomputing device includes, for example, a Blackberry®.

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein. Scope of the invention is thus indicated bythe appended claims, rather than by the foregoing description, and allchanges that come within the meaning and range of equivalency of theclaims are therefore intended to be embraced therein.

The invention claimed is:
 1. A method for classifying objects in motion,comprising: providing, to one or more processors, feature data for oneor more classes of objects to be classified, wherein the feature data isindexed by object class, orientation, and sensor; providing, to the oneor more processors, one or more representative models for characterizingone or more orientation motion profiles for the one or more classes ofobjects in motion; acquiring, via the one or more processors, featuredata for a target object in motion, time of feature data, and trajectoryof the target object in motion; generating, via the one or moreprocessors, reference feature data while the target object is in motionbased on the acquired object class and trajectory of the target objectin motion by: selecting an object class; selecting an orientation motionprofile for the selected object class; for each point in time thatfeature data is collected by the at least one sensor for the targetobject in motion, the selected orientation motion profile and thetrajectory of the target object in motion are used to determine theorientation of the target object in motion, a feature is selected fromthe feature database based on the sensor, object class, and orientationof the target object in motion; classifying, via the one or moreprocessors, the target object during motion of the target object basedon the generated reference feature data and the acquired feature datafor the target object in motion.
 2. The method of claim 1, whereinproviding the one or more representative models for characterizing oneor more orientation motion profiles comprises acquiring orientationmotion data for an exemplary object in motion.
 3. The method of claim 1,wherein providing the one or more representative models forcharacterizing one or more orientation motion profiles comprisesgenerating orientation motion data based on an analytical model for anexemplary object in motion.
 4. The method of claim 1, comprisingacquiring the feature data for the target object in motion and thetrajectory of the target object in motion at one or more instances oftime, periods of time, or a combination of both.
 5. The method of claim1, comprising acquiring the feature data for the target object in motionand the trajectory of the target object in motion using a plurality ofsensors.
 6. The method of claim 1, wherein the feature data comprises atleast one of radar data, optical data or infrared data for each of theone or more classes of objects.
 7. The method of claim 6, wherein thefeature data comprises radar cross section signals and time derivativesof the radar cross section signals.
 8. The method of claim 6, whereinthe sensor is selected from the group consisting of a radar system,lidar system, optical imaging system, or infrared monitoring system. 9.The method of claim 1, comprising classifying the target object usingBayes' Rule the target object as belonging to a particular class of theone or more classes of objects based on the posterior probability thetarget object corresponds to the particular class.
 10. The method ofclaim 1, wherein the feature data for the one or more classes of objectsand the one or more representative models for characterizing one or moreorientation motion profiles for the feature data are indexed in adatabase stored on the processor.
 11. A system for classifying objectsin motion, comprising: data collected prior to classifying a targetobject in motion, the data comprising: a) feature data on one or moreclasses of objects to be classified, wherein the feature data is indexedby orientation, sensor, and object class; and b) one or morerepresentative models for characterizing one or more orientation motionprofiles for the feature data on the one or more classes of objects; atleast one sensor to acquire feature data for a target object in motionand trajectory of the target object in motion; a first processor togenerate reference feature data while the target object is in motionbased on the object class and the trajectory of the target object inmotion, wherein the at least one sensor provides feature data and timeof feature data; a second processor to classify the target object duringmotion of the target object based on the reference feature datagenerated by the first processor, and feature data for the target objectin motion; wherein the first processor generates reference feature databy:
 1. selecting an object class,
 2. selecting an orientation motionprofile for the selected object class,
 3. for each point in time thatfeature data is collected by the at least one sensor for the targetobject in motion, the selected orientation motion profile and thetrajectory of the target object in motion are used to determine theorientation of the target object in motion, a feature is selected fromthe feature database based on the sensor, object class, and orientationof the target object in motion.
 12. The system of claim 11, comprisingone or more sensors to acquire orientation motion data for an exemplaryobject in motion to generate the one or more orientation motion profilesfor the one or more classes of objects.
 13. The system of claim 11,wherein steps 1-3 are repeated to generate a collection of referencefeature vectors.
 14. The system of claim 11, wherein the secondprocessor performs Bayesian classification using the reference featuredata generated by the first processor as a priori data and the featuredata for the target object to be classified to generate posterior objectclass type probabilities.
 15. The method of claim 14, wherein thefeature data for the target object in motion comprises feature datacollected from each sensor at single points in time.